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Emotion Feature Analysis and Recognition Based on Reconstructed EEG Sources | IEEE Journals & Magazine | IEEE Xplore

Emotion Feature Analysis and Recognition Based on Reconstructed EEG Sources


Schematic diagram of EEG-based emotion recognition from both sensor space and source space.

Abstract:

Emotion plays a significant role in perceiving external events or situations in daily life. Due to ease of use and relative accuracy, Electroencephalography (EEG)-based e...Show More

Abstract:

Emotion plays a significant role in perceiving external events or situations in daily life. Due to ease of use and relative accuracy, Electroencephalography (EEG)-based emotion recognition has become a hot topic in the affective computing field. However, scalp EEG is a mixed-signal and cannot directly indicate the exact information about active cortex sources of different emotions. In this paper, we analyze the significant differences of active source regions and frequency bands for pairs of emotions-based reconstructed EEG sources using sLORETA, and 26 Brodmann areas are selected as the regions of interest (ROI). And then, six kinds of time- and frequency-domain features from significant active regions and frequency bands are extracted to classify different emotions using support vector machines. Furthermore, we compare the classification performances of emotion features extracted from active source regions and EEG sensors. We have demonstrated that the features from selected source regions can improve the classification accuracy by extensive experiments on the DEAP and TYUT 2.0 EEG-based datasets.
Schematic diagram of EEG-based emotion recognition from both sensor space and source space.
Published in: IEEE Access ( Volume: 8)
Page(s): 11907 - 11916
Date of Publication: 13 January 2020
Electronic ISSN: 2169-3536

Funding Agency:


References

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